1 Introduction

This paper contains estimates for the effective reproduction number \(R_{t,m}\) over time \(t\) in various countries \(m\) of the world. This is done using the methodology as described in [1]. These have been implemented in R using EpiEstim package [2] which is what is used here. The methodology and assumptions are described in more detail here.

This paper and it’s results should be updated roughly daily and is available online.

As this paper is updated over time this section will summarise significant changes. The code producing this paper is tracked using Git. The Git commit hash for this project at the time of generating this paper was b141aff519ee125929b3b6f686b1070f3fc70742.

2 Data

Data are downloaded from [3]. Minor formatting is applied to get the data ready for further processing.

3 Basic Exploration

Below cumulative case count is plotted on a log scale by continent.

Cumulative Reported Cases by Continent

Cumulative Reported Cases by Continent

Below a 7-day moving average of daily case count is plotted on a log scale by continent.

Daily Reported Cases by Continent (7-day moving average)

Daily Reported Cases by Continent (7-day moving average)

Below cumulative deaths by country is plotted on a log scale:

Cumulative Reported Deaths by Continent

Cumulative Reported Deaths by Continent

Below a 7-day moving average of daily deaths by country is plotted on a log scale:

Daily Deaths by Continent (7-day moving average)

Daily Deaths by Continent (7-day moving average)

4 Method & Assumptions

The methodology is described in detail here.

Countries with populations of less than 500 000 are excluded.

5 Results

5.1 World-wide

Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
cases 5,764,027 2021-04-27 1 1.0 1.0
deaths 90,822 2021-04-27 1 1.1 1.1

5.2 Current reproduction number estimates by country

Below current (last weekly) \(R_{t,m}\) estimates are plotted on a world map.

5.2.0.1 Cases

5.2.1 Deaths

5.3 Top 10 countries

Below we show various extremes of \(R_{t,m}\) where counts (deaths or cases) exceed 50 in the last week.

5.3.1 Lowest \(R_{t,m}\) based on deaths

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Sudan deaths 62 2021-04-27 0.5 0.6 0.8
Slovakia deaths 328 2021-04-27 0.6 0.7 0.8
Palestine deaths 123 2021-04-27 0.6 0.7 0.9
Oman deaths 83 2021-04-27 0.6 0.7 0.9
Czechia deaths 435 2021-04-27 0.7 0.8 0.8
Austria deaths 167 2021-04-27 0.7 0.8 0.9
Peru deaths 2,059 2021-04-27 0.8 0.8 0.8
Jordan deaths 335 2021-04-27 0.7 0.8 0.9
Ethiopia deaths 166 2021-04-27 0.7 0.8 0.9
Guatemala deaths 174 2021-04-27 0.7 0.8 1.0

5.3.2 Lowest \(R_{t,m}\) based on cases

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Equatorial Guinea cases 54 2021-04-27 0.2 0.2 0.3
Cameroon cases 1,189 2021-04-27 0.4 0.4 0.5
Kosovo cases 1,806 2021-04-27 0.5 0.6 0.6
Zimbabwe cases 289 2021-04-27 0.5 0.6 0.7
Zambia cases 376 2021-04-27 0.5 0.6 0.7
Mali cases 476 2021-04-27 0.5 0.6 0.7
Togo cases 274 2021-04-27 0.6 0.7 0.8
Burkina Faso cases 96 2021-04-27 0.6 0.7 0.8
Gabon cases 536 2021-04-27 0.6 0.7 0.8
Poland cases 63,463 2021-04-27 0.7 0.7 0.7

5.3.3 Highest \(R_{t,m}\) based on deaths

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Thailand deaths 55 2021-04-27 2.0 2.8 4.0
Nepal deaths 92 2021-04-27 1.4 1.8 2.2
Honduras deaths 217 2021-04-27 1.4 1.7 2.1
Algeria deaths 60 2021-04-27 1.2 1.6 2.1
Argentina deaths 2,807 2021-04-27 1.4 1.5 1.7
Malaysia deaths 73 2021-04-27 1.2 1.5 1.9
India deaths 18,634 2021-04-27 1.4 1.5 1.6
Cameroon deaths 52 2021-04-27 1.0 1.4 2.1
Ecuador deaths 645 2021-04-27 1.3 1.4 1.5
Georgia deaths 101 2021-04-27 1.1 1.4 1.7

5.3.4 Highest \(R_{t,m}\) based on cases

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Congo cases 594 2021-04-27 3.6 75.8 601.1
Laos cases 451 2021-04-27 3.2 5.8 10.1
Ghana cases 755 2021-04-27 1.9 2.1 2.3
Sri Lanka cases 6,015 2021-04-27 1.7 2.1 2.4
Nepal cases 20,358 2021-04-27 1.8 2.0 2.4
Niger cases 80 2021-04-27 1.4 1.8 2.3
Maldives cases 1,921 2021-04-27 1.5 1.6 1.8
Bhutan cases 87 2021-04-27 1.2 1.5 1.9
Myanmar cases 96 2021-04-27 1.2 1.5 1.8
Rwanda cases 809 2021-04-27 1.3 1.5 1.7

5.4 Risk Quadrants

The plots below show weekly cases (or deaths) on the X-axis and the reproduction number on the Y-axis. By dividing this into 4 quadrants we can identify countries with high cases and high reproduction numbers, or high cases and low reproduction numbers etc.

Values where the reproduction number exceeds 3 are plotted at 3.

5.4.1 Cases

Risk Quadrants - Cases

5.4.2 Deaths

Risk Quadrants - Deaths

5.5 Country Plots by Continent

Below we plot results for each country/province in a list. Values larger than 3 are plotted at 3.

5.5.1 Africa

5.5.1.1 Algeria

5.5.1.2 Angola

5.5.1.3 Benin

5.5.1.4 Botswana

5.5.1.5 Burkina Faso

5.5.1.6 Burundi

5.5.1.7 Cameroon

5.5.1.8 Cape Verde

5.5.1.9 Central African Republic

5.5.1.10 Chad

5.5.1.11 Comoros

5.5.1.12 Congo

5.5.1.13 Cote d’Ivoire

5.5.1.14 Democratic Republic of Congo

5.5.1.15 Djibouti

5.5.1.16 Egypt

5.5.1.17 Equatorial Guinea

5.5.1.18 Eritrea

5.5.1.19 Eswatini

5.5.1.20 Ethiopia

5.5.1.21 Gabon

5.5.1.22 Gambia

5.5.1.23 Ghana

5.5.1.24 Guinea

5.5.1.25 Guinea-Bissau

5.5.1.26 Kenya

5.5.1.27 Lesotho

5.5.1.28 Liberia

5.5.1.29 Libya

5.5.1.30 Madagascar

5.5.1.31 Malawi

5.5.1.32 Mali

5.5.1.33 Mauritania

5.5.1.34 Mauritius

5.5.1.35 Morocco

5.5.1.36 Mozambique

5.5.1.37 Namibia

5.5.1.38 Niger

5.5.1.39 Nigeria

5.5.1.40 Rwanda

5.5.1.41 Senegal

5.5.1.42 Sierra Leone

5.5.1.43 Somalia

5.5.1.44 South Africa

5.5.1.45 South Sudan

5.5.1.46 Sudan

5.5.1.47 Togo

5.5.1.48 Tunisia

5.5.1.49 Uganda

5.5.1.50 Zambia

5.5.1.51 Zimbabwe

5.5.2 Asia

5.5.2.1 Afghanistan

5.5.2.2 Armenia

5.5.2.3 Azerbaijan

5.5.2.4 Bahrain

5.5.2.5 Bangladesh

5.5.2.6 Bhutan

5.5.2.7 Cambodia

5.5.2.8 China

5.5.2.9 Georgia

5.5.2.10 India

5.5.2.11 Indonesia

5.5.2.12 Iran

5.5.2.13 Iraq

5.5.2.14 Israel

5.5.2.15 Japan

5.5.2.16 Jordan

5.5.2.17 Kazakhstan

5.5.2.18 Kuwait

5.5.2.19 Kyrgyzstan

5.5.2.20 Laos

5.5.2.21 Lebanon

5.5.2.22 Malaysia

5.5.2.23 Maldives

5.5.2.24 Mongolia

5.5.2.25 Myanmar

5.5.2.26 Nepal

5.5.2.27 Oman

5.5.2.28 Pakistan

5.5.2.29 Palestine

5.5.2.30 Philippines

5.5.2.31 Qatar

5.5.2.32 Saudi Arabia

5.5.2.33 Singapore

5.5.2.34 South Korea

5.5.2.35 Sri Lanka

5.5.2.36 Syria

5.5.2.37 Taiwan

5.5.2.38 Thailand

5.5.2.39 Timor

5.5.2.40 Turkey

5.5.2.41 United Arab Emirates

5.5.2.42 Uzbekistan

5.5.2.43 Vietnam

5.5.2.44 Yemen

5.5.3 Europe

5.5.3.1 Albania

5.5.3.2 Austria

5.5.3.3 Belarus

5.5.3.4 Belgium

5.5.3.5 Bosnia and Herzegovina

5.5.3.6 Bulgaria

5.5.3.7 Croatia

5.5.3.8 Cyprus

5.5.3.9 Czechia

5.5.3.10 Denmark

5.5.3.11 Estonia

5.5.3.12 Finland

5.5.3.13 France

5.5.3.14 Germany

5.5.3.15 Greece

5.5.3.16 Hungary

5.5.3.17 Ireland

5.5.3.18 Italy

5.5.3.19 Kosovo

5.5.3.20 Latvia

5.5.3.21 Lithuania

5.5.3.22 Luxembourg

5.5.3.23 Moldova

5.5.3.24 Montenegro

5.5.3.25 Netherlands

5.5.3.26 North Macedonia

5.5.3.27 Norway

5.5.3.28 Poland

5.5.3.29 Portugal

5.5.3.30 Romania

5.5.3.31 Russia

5.5.3.32 Serbia

5.5.3.33 Slovakia

5.5.3.34 Slovenia

5.5.3.35 Spain

5.5.3.36 Sweden

5.5.3.37 Switzerland

5.5.3.38 Ukraine

5.5.3.39 United Kingdom

5.5.4 North America

5.5.4.1 Canada

5.5.4.2 Costa Rica

5.5.4.3 Cuba

5.5.4.4 Dominican Republic

5.5.4.5 El Salvador

5.5.4.6 Guatemala

5.5.4.7 Haiti

5.5.4.8 Honduras

5.5.4.9 Jamaica

5.5.4.10 Mexico

5.5.4.11 Nicaragua

5.5.4.12 Panama

5.5.4.13 Trinidad and Tobago

5.5.4.14 United States

5.5.5 Oceania

5.5.5.1 Australia

5.5.5.2 New Zealand

5.5.5.3 Papua New Guinea

5.5.6 South America

5.5.6.1 Argentina

5.5.6.2 Bolivia

5.5.6.3 Brazil

5.5.6.4 Chile

5.5.6.5 Colombia

5.5.6.6 Ecuador

5.5.6.7 Guyana

5.5.6.8 Paraguay

5.5.6.9 Peru

5.5.6.10 Suriname

5.5.6.11 Uruguay

5.5.6.12 Venezuela

5.6 Detailed Output

Detailed output for all countries are saved to a comma-separated value file. The file can be found here.

6 Discussion

Limitation of this method to estimate \(R_{t,m}\) are noted in [1]

  • It’s sensitive to changes in transmissibility, changes in contact patterns, depletion of the susceptible population and control measures.
  • It relies on an assumed generation interval assumptions.
  • The size of the time window can affect the volatility of results.
  • Results are time lagged with regards to true infection, more so in the case of the use of deaths.
  • It’s sensitive to changes in case (or death) detection.
  • The generation interval may change over time.

Further to the above the estimates are made under assumption that the cases and deaths are reported consistently over time. For cases this means that testing needs to be at similar levels and reported with similar lag. Should these change rapidly over an interval of a few weeks the above estimates of the effective reproduction numbers would be biased. For example a rapid expansion of testing over the last 3 weeks would results in overestimating recent effective reproduction numbers. Similarly any changes in reporting (over time and underreporting) of deaths would also bias estimates of the reproduction number estimated using deaths.

Estimates for the reproduction number are plotted in time period in which the relevant measure is recorded. Though in reality the infections giving rise to those estimates would have occurred roughly between a week to 4 weeks earlier depending on whether it was cases or deaths. These figures have not been shifted back.

Despite these limitation we believe the ease of calculation of this method and the ability to use multiple sources makes it useful as a monitoring tool.

7 Author

This report was prepared by Louis Rossouw. Please get in contact with Louis Rossouw if you have comments or wish to receive this regularly.

Louis Rossouw
Head of Research & Analytics
Gen Re | Life/Health Canada, South Africa, Australia, NZ, UK & Ireland
Email: LRossouw@GenRe.com Mobile: +27 71 355 2550

The views in this document represents that of the author and may not represent those of Gen Re. Also note that given the significant uncertainty involved with the parameters, data and methodology care should be taken with these numbers and any use of these numbers.

References

[1] A. Cori, N. M. Ferguson, C. Fraser, and S. Cauchemez, “A new framework and software to estimate time-varying reproduction numbers during epidemics,” American Journal of Epidemiology, vol. 178, no. 9, pp. 1505–1512, Sep. 2013, doi: 10.1093/aje/kwt133. [Online]. Available: https://doi.org/10.1093/aje/kwt133

[2] A. Cori, EpiEstim: A package to estimate time varying reproduction numbers from epidemic curves. 2013 [Online]. Available: https://CRAN.R-project.org/package=EpiEstim

[3] M. Roser, H. Ritchie, E. Ortiz-Ospina, and J. Hasell, “Coronavirus pandemic (COVID-19),” Our World in Data, 2020 [Online]. Available: https://ourworldindata.org/coronavirus. [Accessed: 17-Dec-2020]